Anders Olsson
6437649436
Move team_prior, lhood_lose, lhood_win, inv_buf into ScratchArena so their Vec capacity is reused across games in a Batch. Eliminates 5 per-game heap allocations (the trunc Vec remains local due to borrow constraints with arena.vars). Batch::iteration: 23.0 µs (down from 27.0 µs with naive local Vecs; 8% above T0 21.253 µs baseline due to TruncFactor propagate overhead).
68 lines
3.4 KiB
Plaintext
68 lines
3.4 KiB
Plaintext
# Baseline numbers captured before T0 changes
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# Hardware: lrrr.local / Apple M5 Pro
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# Date: 2026-04-24
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Batch::iteration 29.840 µs
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Gaussian::add 219.58 ps
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Gaussian::sub 219.41 ps
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Gaussian::mul 1.568 ns ← hot path; target ≥1.5× improvement
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Gaussian::div 1.572 ns ← hot path; target ≥1.5× improvement
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Gaussian::pi 262.89 ps
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Gaussian::tau 262.47 ps
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Gaussian::pi_tau_combined 219.40 ps
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# After T0 (2026-04-24, same hardware)
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Batch::iteration 21.253 µs (1.40× — below 3× target; see post-mortem)
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Gaussian::add 218.62 ps (1.00× — unchanged, Add/Sub use moment form)
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Gaussian::sub 220.15 ps (1.00×)
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Gaussian::mul 218.69 ps (7.17× — nat-param: now two f64 adds, no sqrt)
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Gaussian::div 218.64 ps (7.19× — nat-param: now two f64 subs, no sqrt)
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Gaussian::pi 263.19 ps (1.00× — now a field read, same cost)
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Gaussian::tau 263.51 ps (1.00× — now a field read, same cost)
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Gaussian::pi_tau_combined 219.13 ps (1.00×)
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# Post-mortem: Batch::iteration 1.40× vs. 3× target
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#
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# Root cause: the bench has 100 tiny 2-team events. Each event still allocates
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# ~10 Vecs per iteration (down from ~18). The arena covers teams/diffs/ties/margins
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# (was 4 Vecs, now 0 new allocs) but the following remain:
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# - within_priors() returns Vec<Vec<Player<D>>>: 3 Vecs per event (300 total)
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# - event.outputs() returns Vec<f64>: 1 Vec per event (100 total)
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# - sort_perm() allocates 2 scratch Vecs: 200 total
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# - Game::likelihoods = collect() allocates Vec<Vec<Gaussian>>: 4 Vecs (400 total)
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# Total remaining: ~1000 allocs per iteration call vs. ~1800 before (44% reduction).
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#
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# The HashMap → dense Vec win (target 2–4×) benefits the History-level forward/backward
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# sweep, NOT Batch::iteration in isolation — so this bench doesn't show it.
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#
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# To hit ≥3× on Batch::iteration:
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# - Arena-ify sort_perm (use a stack-fixed array for small n_teams)
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# - Pass a within_priors output buffer through the arena
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# - Make Game::likelihoods write into an arena slice rather than allocating
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# These land in T1 (factor graph) when we redesign Game's internals.
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# After T1 (2026-04-24, same hardware)
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Batch::iteration 23.010 µs (1.08× vs T0 21.253 µs — slight regression)
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Gaussian::add 231.23 ps (unchanged)
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Gaussian::sub 235.38 ps (unchanged)
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Gaussian::mul 234.55 ps (unchanged — nat-param storage)
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Gaussian::div 233.27 ps (unchanged)
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Gaussian::pi 272.68 ps (unchanged)
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Gaussian::tau 272.73 ps (unchanged)
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Gaussian::pi_tau_combined 234.xx ps (unchanged)
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# Notes:
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# - Batch::iteration 23.0 µs vs target ≤ 21.5 µs (8% above target).
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# Root cause: TruncFactor::propagate adds one extra Gaussian mul + div per
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# diff vs the old inline EP computation. trunc Vec is still a fresh
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# per-game allocation (borrow checker prevents putting it in the arena
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# alongside vars). These are addressable in T2.
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# - arena.team_prior, lhood_lose, lhood_win, inv_buf, sort_buf all reuse
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# capacity across games (pooled in ScratchArena). sort_perm() allocation
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# eliminated. message.rs deleted.
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# - Gaussian operations unchanged vs T0.
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# - All 53 tests pass. factor graph infrastructure (VarStore, Factor trait,
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# BuiltinFactor, TruncFactor, EpsilonOrMax schedule) in place for T2.
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